Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

To examine the effects of interannual variability on the yellowfin tuna habitat suitability, we calculated the monthly means and standard deviation of sea surface temperature  and absolute dynamic topography of the ocean surface .Then, these monthly means were used to estimate the interannual anomalies, hereafter SSTIA and ADTIA, using the following regression model: where µi is the monthly sea surface temperature or absolute dynamic topography along the time series (i= 1…n), T is the month count (n=264 for the SST, and n=252 for ADT),  is the coefficient representing the long-term linear time trend, and γ represents a seasonal (i.e., cyclical) random effect of the month (M).The interannual anomalies of each variable (SSTIA and ADTIA) were obtained by subtracting the model's predictions from the observed data for a given location and were assigned to each longline set as additional potential predictors of the yellowfin habitat suitability.(black dots).The maps were created with R's package "ggplot2" (https://ggplot2.tidyverse.org/),using the coastlines and political boundaries from the Global Self-consistent, Hierarchical, Highresolution Geography Database (http://www.soest.hawaii.edu/pwessel/gshhg/).

Fig. S14.
Median predictions of yellowfin tuna habitat suitability for 2014 as an example of a negative year at the interannual scale.The high quality (HQH) habitat percentage is the portion of the Gulf of Mexico with habitat suitability > 0.6.The blue line indicates the boundary between Mexican and U.S. Exclusive Economic Zones.Floating dots are the locations of longline sets (black dots).The maps were created with R's package "ggplot2" (https://ggplot2.tidyverse.org/),using the coastlines and political boundaries from the Global Self-consistent, Hierarchical, Highresolution Geography Database (http://www.soest.hawaii.edu/pwessel/gshhg/).

Fig. S2 .
Fig. S2.A) Time series model of monthly means of the sea surface temperature (SST; black dots) in the Gulf of Mexico (GoM).The median model prediction (orange line) and the 95%-credible intervals (orange shaded area) include both the seasonal random effects and the long-term linear trend (black dashed line).B) SST interannual anomalies (SSTIA) in the GoM, which represent the residuals of the model portrayed in panel A.

Fig. S3 .
Fig. S3.Time series model of monthly means of absolute dynamic topography (ADT; black dots) in the Gulf of Mexico (GoM).The median model prediction (orange line) and the 95%credible intervals (orange shaded area) include both the seasonal random effects and the longterm linear trend (black dashed line).

Fig. S4 .
Fig. S4.Frequency distribution of the curved fork length (cm) of yellowfin tuna caught by the Mexican fishery.

Fig. S5 .
Fig. S5.Monthly average (±SD) of the catch per unit effort (fish per 1000 hooks) of yellowfin tuna caught by the Mexican longline fleet in the Gulf of Mexico.

Fig. S6 .
Fig. S6.Annual average (±SD) of the catch per unit effort (fish per 1000 hooks) of yellowfin tuna caught by the Mexican longline fleet in the Gulf of Mexico.

Fig. S7 .
Fig. S7.Frequency distribution of the number of yellowfin tuna caught in each longline set.

Fig. S8 .
Fig. S8.Cleveland dotplot used to visualize outliers.In this graph the row number of an observation is plotted vs. the observation value.

Fig. S9 .
Fig. S9.Pearson's correlation coefficient values between environmental variables.The magnitude of the correlation is represented in the upper right by red circles (negative correlation) and blue circles (positive correlation).

Fig. S12 .
Fig. S12.Median predictions of yellowfin tuna habitat suitability for 2013 as an example of an average year at the interannual scale.The high quality (HQH) habitat percentage is the portion of the Gulf of Mexico with habitat suitability > 0.6.The blue line indicates the boundary between Mexican and U.S. Exclusive Economic Zones.Floating dots are the locations of longline sets

Fig. S16 .
Fig. S16.Median predictions of yellowfin tuna habitat suitability for 2016 as an example of a positive year at the interannual scale.The high quality (HQH) habitat percentage is the portion of the Gulf of Mexico with habitat suitability > 0.6.The blue line indicates the boundary between Mexican and U.S. Exclusive Economic Zones.Floating dots are the locations of longline sets (black dots).The maps were created with R's package "ggplot2" (https://ggplot2.tidyverse.org/),using the coastlines and political boundaries from the Global Self-consistent, Hierarchical, Highresolution Geography Database (http://www.soest.hawaii.edu/pwessel/gshhg/).

Table S1 .
Annual sex ratio of yellowfin tuna caught by the Mexican longline fleet in the Gulf of Mexico between 2012 and 2019.